# Carga de datos
primates_cr_registros <-
st_read(
"https://raw.githubusercontent.com/gf0604-procesamientodatosgeograficos/2021i-datos/main/gbif/primates-cr-registros.csv",
options = c(
"X_POSSIBLE_NAMES=decimalLongitude",
"Y_POSSIBLE_NAMES=decimalLatitude",
quiet = TRUE
)
)
## options: X_POSSIBLE_NAMES=decimalLongitude Y_POSSIBLE_NAMES=decimalLatitude TRUE
## Reading layer `primates-cr-registros' from data source `https://raw.githubusercontent.com/gf0604-procesamientodatosgeograficos/2021i-datos/main/gbif/primates-cr-registros.csv' using driver `CSV'
## Simple feature collection with 4509 features and 50 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -85.96248 ymin: 8.040197 xmax: -82.55949 ymax: 11.15408
## CRS: NA
# Asignacion de CRS
st_crs(primates_cr_registros) = 4326
# Capa geoespacial de cantones
cantones <-
st_read("https://raw.githubusercontent.com/gf0604-procesamientodatosgeograficos/2021i-datos/main/ign/delimitacion-territorial-administrativa/cr_cantones_simp_wgs84.geojson",
quiet = TRUE
)
# Capa geoespacial de provincias
provincias <-
st_read("https://raw.githubusercontent.com/gf0604-procesamientodatosgeograficos/2021i-datos/main/ign/delimitacion-territorial-administrativa/cr_provincias_simp_wgs84.geojson",
quiet = TRUE
)
# Carga de capa de altitud
alt <- getData(
"worldclim",
var = "alt",
res = .5,
lon = -84,
lat = 10
)
altitud <-
alt %>%
crop(provincias) %>%
mask(provincias)
# Carga de capa de color
rcol <- colorNumeric(c("#98FB98", "#808000", "#66CDAA"),
values(altitud),
na.color = "transparent")
# Cruce espacial con la tabla de cantones, para obtener el nombre del cantón
primates_cr_registros <-
primates_cr_registros %>%
st_join(cantones["canton"])
# Tabla de registros de presencia
primates_cr_registros%>%
st_drop_geometry() %>%
select(stateProvince,canton,family, species, eventDate) %>%
datatable(
colnames = c("Provincia", "Canton", "Especie", "Familia", "Fecha"),
options = list( searchHighlight = TRUE,language = list(url = '//cdn.datatables.net/plug-ins/1.10.11/i18n/Spanish.json')
)
)
## Filtro para contar especies
mono_ardilla <- primates_cr_registros %>%
dplyr::select(stateProvince,canton,family, species, eventDate) %>%
filter(species == "Saimiri oerstedii")
mono_aullador <- primates_cr_registros %>%
dplyr:: select(stateProvince,canton,family, species, eventDate) %>%
filter(species == "Alouatta palliata")
mono_carablanca <- primates_cr_registros %>%
dplyr:: select(stateProvince,canton,family, species, eventDate)%>%
filter(species == "Cebus capucinus")
mono_arana <- primates_cr_registros %>%
dplyr::select(stateProvince,canton,family, species, eventDate)%>%
filter(species == "Ateles geoffroyi Kuhl")
## Grafico pastel
primates_cr_registros%>%
plot_ly(
labels = ~c("mono_ardilla","mono_aullador","mono_carablanca","mono_arana"
),
values = ~c(453, 1994, 1463, 599),
type = "pie")%>%
config(locale = "es")%>%
layout(
title = "",
xaxis = list(showgrid = FALSE,zeroline = FALSE,showticklabels = FALSE
),
yaxis = list( showgrid = FALSE,zeroline = FALSE, showticklabels = FALSE
)
)
au <- primates_cr_registros %>%
dplyr::select(species,
stateProvince,
canton,
eventDate) %>%
filter(species == "Alouatta palliata")
ard <- primates_cr_registros %>%
dplyr::select(species,
stateProvince,
canton,
eventDate) %>%
filter(species == "Saimiri oerstedii")
ara <- primates_cr_registros %>%
dplyr::select(species,
stateProvince,
canton,
eventDate) %>%
filter(species == "Ateles geoffroyi")
cara <- primates_cr_registros %>%
dplyr::select(species,
stateProvince,
canton,
eventDate) %>%
filter(species == "Cebus capucinus")
mau <- paste0((au$species),
(au$stateProvince),
(au$canton),
(au$eventDate))
mard <- paste0((ard$species),
(ard$stateProvince),
(ard$canton),
(ard$eventDate))
mara <- paste0((ara$species),
(ara$stateProvince),
(ara$canton),
(ara$eventDate))
mcara <- paste0((cara$species),
(cara$stateProvince),
(cara$canton),
(cara$eventDate))
primates_cr_registros %>%
leaflet() %>%
addProviderTiles(providers$OpenStreetMap.Mapnik, group = "OpenStreetMap") %>%
addProviderTiles(providers$Stamen.TonerLite, group = "Stamen Toner Lite") %>%
addProviderTiles(providers$Esri.WorldImagery, group = "Imágenes de ESRI") %>%
addRasterImage(
altitud,
colors = rcol,
opacity = 0.8,
group = "Altitud") %>%
addCircleMarkers(
data = au,
stroke = F,
radius = 4,
fillColor = "#FFA07A",
fillOpacity = 1,
popup = mau,
group = "Alouatta palliata"
) %>%
addCircleMarkers(
data = ard,
stroke = F,
radius = 4,
fillColor = "#FFA500",
fillOpacity = 1,
popup = mard,
group = "Saimiri oerstedii"
) %>%
addCircleMarkers(
data = ara,
stroke = F,
radius = 4,
fillColor = " #4B0082",
fillOpacity = 1,
popup = mara,
group = "Ateles geoffroyi"
) %>%
addCircleMarkers(
data = cara,
stroke = F,
radius = 4,
fillColor = " #00FF00",
fillOpacity = 1,
popup = mcara,
group = "Cebus capucinus"
) %>%
addLayersControl(
baseGroups = c("OpenStreetMap", "Stamen Toner Lite",
"Imágenes de ESRI"),
overlayGroups = c("Mono Aullador", "Mono Ardilla",
"Mono Araña", "Mono Carablanca"
,"Altitud")
) %>%
addMiniMap(tiles = providers$Stamen.OpenStreetMap.Mapnik,
position = "bottomleft",
toggleDisplay = TRUE
)